Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/339824
Type: Artigo
Title: Quasi-maximum likelihood estimation of GARCH models in the presence of missing values
Author: Cascone, Marcos H.
Hotta, Luiz K.
Abstract: This work presents a new method to deal with missing values in financial time series. Previous works are generally based in state-space models and Kalman filter and few consider ARCH family models. The traditional approach is to bound the data together and perform the estimation without considering the presence of missing values. The existing methods generally consider missing values in the returns. The proposed method considers the presence of missing values in the price of the assets instead of in the returns. The performance of the method in estimating the parameters and the volatilities is evaluated through a Monte Carlo simulation. Value at risk is also considered in the simulation. An empirical application to NASDAQ 100 Index series is presented
Subject: Séries temporais
Country: Reino Unido
Editor: Taylor & Francis
Rights: Fechado
Identifier DOI: 10.1080/00949655.2018.1546860
Address: https://www.tandfonline.com/doi/full/10.1080/00949655.2018.1546860
Date Issue: 2019
Appears in Collections:IMECC - Artigos e Outros Documentos

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